A systematic review of propensity score methods in the acute care surgery literature: avoiding the pitfalls and proposing a set of reporting guidelines.

Abstract

Propensity score methods are techniques commonly employed in observational research to account for confounding when estimating the effects of treatments and exposures. These methods have been increasingly employed in the acute care surgery literature in an attempt to infer causality; however, the adequacy of reporting and the appropriateness of statistical analyses when using propensity score matching remain unclear. The goal of this systematic review is to assess the adequacy of reporting of propensity score methods, with an emphasis on propensity score matching (to assess balance and the use of appropriate statistical tests), in acute care surgery (ACS) studies and to provide suggestions for improvement for junior investigators. We searched three databases, and other relevant literature (from January 2005 to June 2015) to identify observational studies within the ACS literature using propensity score methods (PROSPERO No: CRD42016036432). Two reviewers extracted data and assessed the quality of the studies retrieved by reviewing the adequacy of both overall reporting and of the propensity score matching methods used. A total of 49/71 (69%) of studies adequately reported propensity score methods overall. Matching was the most common propensity score method used in 46/71 (65%) studies, with 36/46 (78%) studies reporting matching methods adequately. Only 19/46 (41%) of matching studies reported the balance of baseline characteristics between treated and untreated subjects while 6/46 (13%) used correct statistical methods to assess balance. There were 35/46 (76%) of matching studies that explicitly used statistical methods appropriate for the analysis of matched data when estimating the treatment effect and its statistical significance. We have proposed reporting guidelines for the use of propensity score methods in the acute care surgery literature. This is to help investigators improve the adequacy of reporting and statistical analyses when using observational data to estimate effects of treatments and exposures.